{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "45f8b035-b679-4290-ada9-1f49f55dd1af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                          | 0/400 [00:00<?, ?it/s]C:\\Users\\wangyifan\\AppData\\Local\\Temp\\ipykernel_2308\\2407528264.py:43: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  x_layer = torch.nn.functional.softmax(x_layer)\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 400/400 [00:07<00:00, 52.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time is 0:00:07.590865 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000 0.5626318\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1、导入包和定义参数\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as tud\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import trange\n",
    "EPOCHES = 400\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "\n",
    "#2、定义数据，这里是自己建立\n",
    "torch.manual_seed(1)\n",
    "n_data = torch.ones(100, 2)     #内容为一个 100 行 2 列 的 tensor\n",
    "x0 = torch.normal(3*n_data, 1)  \n",
    "y0 = torch.zeros(100)  \n",
    "x1 = torch.normal(-2*n_data, 1)  \n",
    "y1 = torch.ones(100)\n",
    "x2 = torch.normal(10*n_data, 1)\n",
    "y2 = torch.ones(100)*2\n",
    "x = torch.cat((x0, x1,x2)).type(torch.FloatTensor)  \n",
    "y = torch.cat((y0, y1,y2)).type(torch.LongTensor) \n",
    "\n",
    "torch_dataset = tud.TensorDataset(x,y)\n",
    "loader = tud.DataLoader(\n",
    "    dataset=torch_dataset,\n",
    "    batch_size=32,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "if USE_CUDA:\n",
    "    x = x.cuda()\n",
    "    y = y.cuda()\n",
    "    \n",
    "#3、定义网络和模型\n",
    "#两层的网络\n",
    "class Net(torch.nn.Module):  \n",
    "    def __init__(self, n_feature, n_hidden, n_output):  \n",
    "        super(Net, self).__init__()  \n",
    "        self.n_hidden = torch.nn.Linear(n_feature, n_hidden)  \n",
    "        self.out = torch.nn.Linear(n_hidden, n_output)  \n",
    "    def forward(self, x_layer):  \n",
    "        x_layer = torch.relu(self.n_hidden(x_layer))  \n",
    "        x_layer = self.out(x_layer) \n",
    "        x_layer = torch.nn.functional.softmax(x_layer)  \n",
    "        return x_layer  \n",
    "\n",
    "model = Net(n_feature=2, n_hidden=20, n_output=3)  \n",
    "if USE_CUDA:\n",
    "    model = model.cuda()\n",
    "# print(net)  \n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.02)  \n",
    "loss_func = torch.nn.CrossEntropyLoss() \n",
    "\n",
    "# 4、 完成训练和迭代\n",
    "from datetime import  datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "losses = []\n",
    "for i in trange(EPOCHES):\n",
    "    for step, (batch_x,batch_y) in enumerate(loader):\n",
    "        x_b = batch_x\n",
    "        y_b = batch_y\n",
    "        if USE_CUDA:\n",
    "            x_b = x_b.cuda()\n",
    "            y_b = y_b.cuda()\n",
    "        out = model(x_b)  \n",
    "    # print(out.shape, y.shape)  \n",
    "        loss = loss_func(out, y_b) \n",
    "        losses.append(loss.cpu().detach().numpy())\n",
    "        optimizer.zero_grad()  \n",
    "        loss.backward()  \n",
    "        optimizer.step() \n",
    "\n",
    "end_time = datetime.now()       \n",
    "msecs = (end_time - start_time) \n",
    "print(f\"time is {msecs} s\" )\n",
    "\n",
    "#5、其它--打印损失\n",
    "x1 = range(0,len(losses))\n",
    "plt.plot(x1,losses,\".-\")\n",
    "plt.xlabel(\"epoches\")\n",
    "plt.ylabel(\"test loss\")\n",
    "plt.show()\n",
    "\n",
    "#打印下损失\n",
    "print(len(losses),losses[-1])\n",
    "\n",
    "#6、其它--结果打印 \n",
    "train_result = model(x)  \n",
    "\n",
    "# print(train_result.shape)  \n",
    "train_predict = torch.max(train_result, 1)[1]  \n",
    "x_  = x.data.cpu().numpy()\n",
    "plt.scatter(x_[:, 0],x_[:, 1], c=train_predict.cpu().data.numpy(), s=100, lw=0, cmap='RdYlGn')  \n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "918cb7a7-fc8d-442f-9522-6b35474953ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.9945, 0.0028, 0.0026], device='cuda:0', grad_fn=<SelectBackward0>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_result[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13581e1f-510f-4098-875b-a041258d8232",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d93afb0f-dc61-423a-8ff9-664b61611574",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a35606e-40b5-4e6b-9a60-0b939f8eebc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1、导入包和定义参数\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as tud\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import trange\n",
    "EPOCHES = 400\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "\n",
    "#2、定义数据，这里是自己建立\n",
    "torch.manual_seed(1)\n",
    "n_data = torch.ones(100, 2)     #内容为一个 100 行 2 列 的 tensor\n",
    "x0 = torch.normal(3*n_data, 1)  \n",
    "y0 = torch.zeros(100)  \n",
    "x1 = torch.normal(-2*n_data, 1)  \n",
    "y1 = torch.ones(100)\n",
    "x2 = torch.normal(10*n_data, 1)\n",
    "y2 = torch.ones(100)*2\n",
    "x = torch.cat((x0, x1,x2)).type(torch.FloatTensor)  \n",
    "y = torch.cat((y0, y1,y2)).type(torch.LongTensor) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e005ee5-54df-44b1-a7aa-7435b024316f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch\n",
    "\n",
    "class TensorDataset(Dataset):\n",
    "    # TensorDataset继承Dataset, 重载了__init__, __getitem__, __len__\n",
    "    # 实现将一组Tensor数据对封装成Tensor数据集\n",
    "    # 能够通过index得到数据集的数据，能够通过len，得到数据集大小\n",
    "\n",
    "    def __init__(self, data_tensor, target_tensor):\n",
    "        self.data_tensor = data_tensor\n",
    "        self.target_tensor = target_tensor\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.data_tensor[index], self.target_tensor[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.data_tensor.size(0)\n",
    "\n",
    "\n",
    "# 将数据封装成Dataset\n",
    "tensor_dataset = TensorDataset(x,y)\n",
    "\n",
    "\n",
    "loader2 = tud.DataLoader(\n",
    "    dataset=torch_dataset,\n",
    "    batch_size=32,\n",
    "   # shuffle=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "54cd6883-db6d-4da7-9ee6-e3b45f0b045e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                          | 0/400 [00:00<?, ?it/s]C:\\Users\\wangyifan\\AppData\\Local\\Temp\\ipykernel_2308\\1289186083.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  x_layer = torch.nn.functional.softmax(x_layer)\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 400/400 [00:18<00:00, 21.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time is 0:00:18.463142 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000 0.55389893\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if USE_CUDA:\n",
    "    x = x.cuda()\n",
    "    y = y.cuda()\n",
    "    \n",
    "#3、定义网络和模型\n",
    "#两层的网络\n",
    "class Net(torch.nn.Module):  \n",
    "    def __init__(self, n_feature, n_hidden, n_output):  \n",
    "        super(Net, self).__init__()  \n",
    "        self.n_hidden = torch.nn.Linear(n_feature, n_hidden)  \n",
    "        self.out = torch.nn.Linear(n_hidden, n_output)  \n",
    "    def forward(self, x_layer):  \n",
    "        x_layer = torch.relu(self.n_hidden(x_layer))  \n",
    "        x_layer = self.out(x_layer) \n",
    "        x_layer = torch.nn.functional.softmax(x_layer)  \n",
    "        return x_layer  \n",
    "\n",
    "model = Net(n_feature=2, n_hidden=20, n_output=3)  \n",
    "if USE_CUDA:\n",
    "    model = model.cuda()\n",
    "# print(net)  \n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.02)  \n",
    "loss_func = torch.nn.CrossEntropyLoss() \n",
    "\n",
    "# 4、 完成训练和迭代\n",
    "from datetime import  datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "losses = []\n",
    "for i in trange(EPOCHES):\n",
    "    for step, (batch_x,batch_y) in enumerate(loader2):\n",
    "        x_b = batch_x\n",
    "        y_b = batch_y\n",
    "        if USE_CUDA:\n",
    "            x_b = x_b.cuda()\n",
    "            y_b = y_b.cuda()\n",
    "        out = model(x_b)  \n",
    "    # print(out.shape, y.shape)  \n",
    "        loss = loss_func(out, y_b) \n",
    "        losses.append(loss.cpu().detach().numpy())\n",
    "        optimizer.zero_grad()  \n",
    "        loss.backward()  \n",
    "        optimizer.step() \n",
    "\n",
    "end_time = datetime.now()       \n",
    "msecs = (end_time - start_time) \n",
    "print(f\"time is {msecs} s\" )\n",
    "\n",
    "#5、其它--打印损失\n",
    "x1 = range(0,len(losses))\n",
    "plt.plot(x1,losses,\".-\")\n",
    "plt.xlabel(\"epoches\")\n",
    "plt.ylabel(\"test loss\")\n",
    "plt.show()\n",
    "\n",
    "#打印下损失\n",
    "print(len(losses),losses[-1])\n",
    "\n",
    "#6、其它--结果打印 \n",
    "train_result = model(x)  \n",
    "\n",
    "# print(train_result.shape)  \n",
    "train_predict = torch.max(train_result, 1)[1]  \n",
    "x_  = x.data.cpu().numpy()\n",
    "plt.scatter(x_[:, 0],x_[:, 1], c=train_predict.cpu().data.numpy(), s=100, lw=0, cmap='RdYlGn')  \n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8e42a2f-2a83-434d-985b-5ee3fc3b6bdb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
